The Machine Learning Engineer is responsible for designing, developing, optimizing, testing and deploying machine learning to support experimentation and innovation to help build cutting edge artificial intelligence solutions for the Enterprise. Incumbent must be adept about exploring and visualizing data to understand its quality and identifying differences that could affect performance when deploying the model.
Education & Experience:
1. Must meet one of the following:
Field of study: math, science, statistics, economics, finance, informatics, computer science, information systems, health information, epidemiology, data analytics, data science, predictive analytics, artificial intelligence, engineering, physics or related fields.
Knowledge of open-source application stack commonly used in data science workflows (Pandas, Spark, Sci-kit Learn, TensorFlow, Keras)
Experience: analytics, high-level programming, predictive modeling, relational databases, big data, open-source software, feature engineering, data transformation, ETL, API development, data processing, statistical analysis, data exploration.
2. Certifications preferred: Designated certification may be used in lieu of some experience i.e. Coursera IBM Data Science, Data Engineering with GCP, IBM Applied AI, or IBM Applied Engineering.
3. Specialized training in Data Engineering, Data Science, Machine Learning, Big Data Platforms (Spark, Kafka, Hadoop, H2O, etc.), High Level Programming in Data Engineering, Data Science, or Machine Learning (Python, Scala, Java, R, SAS for Data Science, etc).
Specialized Knowledge & Skills